# Semantic Models for the First-stage Retrieval: A Comprehensive Review

arXiv: https://arxiv.org/abs/2103.04831 · HTML: https://mlsanity.com/paper/2103.04831/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 30 of 57)

- [A Survey on Retrieval-Augmented Text Generation for Large Language Models](https://mlsanity.com/paper/2404.10981/) (2024)
- [When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation](https://mlsanity.com/paper/2402.11457/) (2024)
- [ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval](https://mlsanity.com/paper/2205.09153/) (2022)
- [Neural Retrievers are Biased Towards LLM-Generated Content](https://mlsanity.com/paper/2310.20501/) (2023)
- [UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems](https://mlsanity.com/paper/2401.13256/) (2024)
- [Towards Robust Ranker for Text Retrieval](https://mlsanity.com/paper/2206.08063/) (2022)
- [CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks](https://mlsanity.com/paper/2208.07652/) (2022)
- [Fine-Grained Distillation for Long Document Retrieval](https://mlsanity.com/paper/2212.10423/) (2022)
- [Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method](https://mlsanity.com/paper/2308.09861/) (2023)
- [Scaling Laws For Dense Retrieval](https://mlsanity.com/paper/2403.18684/) (2024)
- [Large Language Models are Strong Zero-Shot Retriever](https://mlsanity.com/paper/2304.14233/) (2023)
- [Towards Completeness-Oriented Tool Retrieval for Large Language Models](https://mlsanity.com/paper/2405.16089/) (2024)
- [A Survey for Efficient Open Domain Question Answering](https://mlsanity.com/paper/2211.07886/) (2022)
- [UnifieR: A Unified Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2205.11194/) (2022)
- [ConTextual Masked Auto-Encoder for Dense Passage Retrieval](https://mlsanity.com/paper/2208.07670/) (2022)
- [LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2208.13661/) (2022)
- [OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries](https://mlsanity.com/paper/2211.12850/) (2022)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods](https://mlsanity.com/paper/2402.13350/) (2024)
- [Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models](https://mlsanity.com/paper/2108.04049/) (2021)
- [Query-as-context Pre-training for Dense Passage Retrieval](https://mlsanity.com/paper/2212.09598/) (2022)
- [Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems](https://mlsanity.com/paper/2402.12784/) (2024)
- [On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective](https://mlsanity.com/paper/2306.12756/) (2023)
- [A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking](https://mlsanity.com/paper/2505.02171/) (2025)
- [Binary Embedding-based Retrieval at Tencent](https://mlsanity.com/paper/2302.08714/) (2023)
- [Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models](https://mlsanity.com/paper/2310.05628/) (2023)
- [CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval](https://mlsanity.com/paper/2311.02834/) (2023)
- [BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?](https://mlsanity.com/paper/2411.12235/) (2024)
- [CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval](https://mlsanity.com/paper/2304.03158/) (2023)

## Abstract (arXiv)

Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts during past decades, the first-stage retrieval has long been dominated by classical term-based models. Unfortunately, these models suffer from the vocabulary mismatch problem, which may block re-ranking stages from relevant documents at the very beginning. Therefore, it has been a long-term desire to build semantic models for the first-stage retrieval that can achieve high recall efficiently. Recently, we have witnessed an explosive growth of research interests on the first-stage semantic retrieval models. We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development. In this paper, we describe the current landscape of the first-stage retrieval models under a unified framework to clarify the connection between classical term-based retrieval methods, early semantic retrieval methods and neural semantic retrieval methods. Moreover, we identify some open challenges and envision some future directions, with the hope of inspiring more researches on these important yet less investigated topics.